Using Serum Amino Acids to Predict Traumatic Brain Injury: A Systematic Approach to Utilize Multiple Biomarkers

• Type of Material: Article
• Authors: Marzieh Hajiaghamemar, Georgia Institute of Technology; Todd Kilbaugh, University of Pennsylvania; Kristy B. Arbogast, University of Pennsylvania; Christina L. Master, University of Pennsylvania; Susan Margulies, Emory University
• Language: English
• Date: 2020-03-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/ijms21051786
• Title of Journal or Parent Work: International Journal of Molecular Sciences
• Volume: 21
• Issue: 5
• Grant/Funding Information: This research was funded by the National Institutes of Health (NIH) grant R01NS097549 and National Institute of Neurological Disorders and Stroke (NINDS) grant U01NS069545.
• Keywords: data science; Classification; Cerebrospinal fluid; Chemistry; Taurine; trauma; head injury; Biochemistry & Molecular Biology; Hemorrhage; Glutamate; multivariate metrics; Life Sciences & Biomedicine; biomarker; Physical Sciences; Science & Technology; Chemistry, Multidisciplinary; Children; diagnostics
• Research Categories: Engineering, Biomedical; Chemistry, Biochemistry; Biology, Neuroscience; Health Sciences, Medicine and Surgery

Abstract

Traumatic brain injury (TBI) can cause biochemical and metabolomic alterations in the brain tissue and serum. These alterations can be used for diagnosis and prognosis of TBI. Here, the serum concentrations of seventeen amino acids (AA) were studied for their potential utility as biomarkers of TBI. Twenty-five female, 4-week-old piglets received diffuse (n = 13) or focal (n = 12) TBI. Blood samples were obtained both pre-injury and at either 24-h or 4-days post-TBI. To find a robust panel of biomarkers, the results of focal and diffuse TBIs were combined and multivariate logistic regression analysis, coupled with the best subset selection technique and repeated k-fold cross-validation method, was used to perform a thorough search of all possible subsets of AAs. The combination of serum glycine, taurine, and ornithine was optimal for TBI diagnosis, with 80% sensitivity and 86% overall prediction rate, and showed excellent TBI diagnostic performance, with 100% sensitivity and 78% overall prediction rate, on a separate validation dataset including four uninjured and five injured animals. We found that combinations of biomarkers outperformed any single biomarker. We propose this 3-AA serum biomarker panel to diagnose mild-to-moderate focal/diffuse TBI. The systematic approaches implemented herein can be used for combining parameters from various TBI assessments to develop/evaluate optimal multi-factorial diagnostic/prognostic TBI metrics.